Last updated: 2023-03-08

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knitr::opts_chunk$set(echo = TRUE, warning = F, message = F)

library(tidyverse)
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Recheck list of risdi/brana-specific introns

I previously made a list of risdiplam/branaplam/C2C5-specific introns based on the dose response experiment. Because the different molecules have different genome-wide effective potencies (ie, 100nM C2C5 is different than 100nM risdiplam, despite them having generally similar specificity profiles), I tested for a difference in the EC50 of each intron for each traitment pair, relative to the genome-wide median. You can see that in a previous notebook I plotted the dose-response datapoints for a random sample of 20 of these significant molecule-specific effects, and they are generally believable effects from those.

GA.GT.Introns <- read_tsv("../output/EC50Estimtes.FromPSI.txt.gz")

ColorKey <- c("Branaplam-specific"="#005A32", "Risdiplam-specific"="#084594", "Non significant"="#969696")

P1.dat <- GA.GT.Introns %>%
  mutate(Color = case_when(
    EC.Ratio.Test.Estimate.FDR_Branaplam.Risdiplam > 0.05 ~ "Non significant",
    ECRatio.ComparedToGenomewideMedian_Branaplam.Risdiplam > 1 ~ "Risdiplam-specific",
    ECRatio.ComparedToGenomewideMedian_Branaplam.Risdiplam < 1 ~ "Branaplam-specific"
  ))

ggplot(P1.dat, aes(x=ED50_Branaplam, y=ED50_Risdiplam, color=Color)) +
  geom_point() +
  scale_x_continuous(trans='log10', limits = c(1, 1E7)) +
  scale_y_continuous(trans='log10', limits = c(1, 1E7)) +
  scale_color_manual(values=ColorKey) +
  labs(x= "Branaplam\nED50 (nanomolar)", y= "Risdiplam\nED50 (nanomolar)", color=NULL) +
  theme(legend.position='bottom') +
  theme_bw()

Version Author Date
ea36e65 Benjmain Fair 2023-03-07 
P1.dat %>%
  count(Color)
# A tibble: 4 × 2
  Color                  n
  <chr>              <int>
1 Branaplam-specific   240
2 Non significant      200
3 Risdiplam-specific   161
4 <NA>                2878

Ok, so each point is a branaplam-specific or risdiplam-specific GA-GT intron. There are 200 non-significant points, 161 risdiplam-specific points, and 240 branaplam-specific points. I think it will be useful for Jingxin to be able to easily visualize these before validating them… Some introns that change PSI from 1% to 3% might be very significant, but not the best for validation. Furthermore, not all of these events might be involved in poison exon inclusion. Some of them might just be alt 5’ss or something. And these details might inform which are the best candidates for validation by flanking PCR primers. Also, when looking at the data, some of the ‘risdiplam-specific’ introns I called are just not believable effects. Like the dose-response data points might just be too noisy. I did a reasonable job of filtering these out, and trying to make the FDR estimate reasonably calibrated… Or like there are some examples where it appears ‘risdiplam-specific’ but it is because the cryptic event is only just barely visible at the highest dose of risdiplam, and not visible in any of the branaplam doses (so it just requires a really really high dose to see the effect). So for all these reasons, it will be best to check the raw coverage data first… So I’ll write the out this list as a bedfile for easy viewing in IGV, a save a session that has the bedfile loaded, along with bigwigs for the dose response experiment so hopefully it will be easy to view.

P1.dat %>%
  dplyr::select(chrom=`#Chrom`, start, end, junc, score=ECRatio.ComparedToGenomewideMedian_Branaplam.Risdiplam, strand=strand.y, Color) %>%
  filter(!is.na(score)) %>%
  mutate(thickStart = start, thickEnd=end, Color=recode(Color, !!!ColorKey)) %>%
  mutate(RgbCol = apply(col2rgb(Color), 2, paste, collapse=',')) %>%
  dplyr::select(chrom:strand, thickStart, thickEnd, RgbCol) %>%
  arrange(chrom, start, end) %>%
  write_tsv("../code/Branaplam_Risdiplam_specific_introns.bed", col_names = F)
bgzip ../code/Branaplam_Risdiplam_specific_introns.bed
tabix -p bed ../code/Branaplam_Risdiplam_specific_introns.bed.gz

So to view, you will need to use the globus link I shared with Jingxin to download the code directory, or at minimum download the code/bigwigs folder of bigwig files, the code/tracks.xml IGV session, and the code/Branaplam_Risdiplam_specific_introns.bed.gz and code/Branaplam_Risdiplam_specific_introns.bed.gz.tbi. Then, from IGV, go to File >> OpenSession and open the tracks.xml. The tracks.xml uses relative filepaths so you need to preserve the relative filepaths of the thing you downloaded (that is, they need to all be in the same folder, such as a folder named code).

Now you should be able to select the Branaplam_Risdiplam_specific_introns.bed track, and Ctl-F or Ctl-B to jump to the next GA-GT introns, colored according to significance. Browse around to find coordinates of potential splice events for validation. Note that each bigwig is already normalized signal relative to library depth. With autoscaling for the y-axis turned on (the default), it may be visually obvious to see the poison exons, but it might not be totally obvious to see if there are effects on gene expression. To better visualize effects on host gene expression, I recommend just selecting all bigwig tracks and right-click to “group autoscale”. Since coverage of each track is already normalized by library size, it should be easy to visually interpret if there is a change in gene expression.

Here are screenshots of a few examples…

BranaSpecific1

BranaSpecific2

RisdiSpecific1

In general, I’m noticing there a quite a few believable branaplam-specific examples but the risdlam-specific are for the most part very small specificity effect sizes. That is, there are very few obviously risdiplam-specific events, compared to branaplam-specific events.

Let’s list the top risdi-specific introns in the following table:

P1.dat %>%
  arrange(desc(ECRatio.ComparedToGenomewideMedian_Branaplam.Risdiplam)) %>%
  head() %>%
  knitr::kable()
junc #Chrom start end gid strand.y seq Donor.score gene_names gene_ids SpliceDonor UpstreamSpliceAcceptor IntronType Steepness LowerLimit UpperLimit ED50_Branaplam ED50_C2C5 ED50_Risdiplam spearman.coef.Branaplam spearman.coef.C2C5 spearman.coef.Risdiplam EC.Ratio.Test.Estimate_Branaplam.C2C5 EC.Ratio.Test.Estimate_Branaplam.Risdiplam EC.Ratio.Test.Estimate_C2C5.Risdiplam ECRatio.ComparedToGenomewideMedian_Branaplam.C2C5 ECRatio.ComparedToGenomewideMedian_Branaplam.Risdiplam ECRatio.ComparedToGenomewideMedian_C2C5.Risdiplam EC.Ratio.Test.Estimate.P_Branaplam.C2C5 EC.Ratio.Test.Estimate.P_Branaplam.Risdiplam EC.Ratio.Test.Estimate.P_C2C5.Risdiplam EC.Ratio.Test.Estimate.FDR_Branaplam.C2C5 EC.Ratio.Test.Estimate.FDR_Branaplam.Risdiplam EC.Ratio.Test.Estimate.FDR_C2C5.Risdiplam Color
chr2:72179461:72182894:clu_4568_- chr2 72179461 72182894 chr2_clu_4568_- - GAGAGTAAGTA 6.005894 EXOC6B ENSG00000144036.16 chr2.72182894.- chr2.72182903.- Annotated -2.7570544 3.1361062 31.384932 2.495499e+09 248.12468 785.0629479 0.2277339 0.5594024 0.9139077 1.005744e+07 3178724.1044 0.3160571 7.482724e+06 25547101.480 3.0610287 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 Risdiplam-specific
chr13:20404741:20405904:clu_25569_- chr13 20404741 20405904 chr13_clu_25569_- - GAGAGTAAGTC 5.446634 CRYL1 ENSG00000165475.15 chr13.20405904.- chr13.20405997.- Alt 5’ss -2.8191629 3.3431713 48.302244 1.641993e+09 1400.00395 4071.6117233 0.1740777 0.6024948 0.9128709 1.172849e+06 403278.4047 0.3438452 8.725985e+05 3241109.952 3.3301579 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 Risdiplam-specific
chr9:108931201:108933271:clu_18237_- chr9 108931201 108933271 chr9_clu_18237_- - AAGAGTAAGCA 4.905988 ELP1 ENSG00000070061.15 chr9.108933271.- chr9.108933405.- Alt 5’ss -9.6753286 0.5638888 7.339398 2.540583e+07 33.32162 895.8671341 0.1926687 0.9052038 0.7822166 7.624427e+05 28358.9229 0.0371948 5.672567e+05 227917.950 0.3602338 0.0000021 0.0000000 0.0000000 0.0000023 0.0000000 0.0000000 Risdiplam-specific
chr18:10773629:10774006:clu_33760_- chr18 10773629 10774006 chr18_clu_33760_- - ATGAGTAAGTC 4.559120 PIEZO2 ENSG00000154864.12 chr18.10774006.- chr18.10774036.- Annotated -2.2089456 0.0687500 51.453519 1.001587e+07 143.33677 1424.8728605 0.2738613 0.9128709 0.8416254 6.987650e+04 7029.3086 0.1005962 5.198805e+04 56493.880 0.9742791 0.0000000 0.0000000 0.4229854 0.0000000 0.0000000 0.6497405 Risdiplam-specific
chr19:10330526:10332870:clu_34319_- chr19 10330526 10332870 chr19_clu_34319_- - GAGAGTACGCC 2.214523 RAVER1 ENSG00000161847.14 chr19.10332870.- chr19.10332909.- Alt 5’ss -0.0000251 0.1685537 3.589164 1.413434e+03 22.81686 0.7759248 0.9831921 -0.2712254 -0.5135291 6.194690e+01 1821.6118 29.4060210 4.608844e+01 14640.120 284.7988165 0.9969948 0.9965823 0.9983501 0.4873920 0.4546814 1.0000000 Non significant
chr4:41014398:41032584:clu_10225_- chr4 41014398 41032584 chr4_clu_10225_- - AGGAGTAGGTG 5.043083 APBB2 ENSG00000163697.17 chr4.41032584.- chr4.41032626.- Alt 5’ss -3.0030040 0.0355949 1.682290 3.820296e+05 22.66113 452.1565311 0.3195048 0.8580987 0.9082951 1.685837e+04 844.9056 0.0501179 1.254261e+04 6790.426 0.4853942 0.0010952 0.0000000 0.0006567 0.0009786 0.0000000 0.0025582 Risdiplam-specific

After looking at some of the top risdiplam-specific hits from the list above, here is perhaps the most clear example:

RisdiSpecific2

Update, 20230308

Yang suggested I do some more exploring/filtering of the data a bit more to see if there are any introns and interesting host genes with reasonable differences in specificity between the risdiplam and branaplam treatments. So like HTT should show up here for example, but I wonder what other interesting genes may be on the list. I want to get a more useful/filtered list for Yang and Jingxin to browse

ColorKey <- c("Branaplam-specific"="#005A32", "Risdiplam-specific"="#084594", "Non significant"="#969696")


P.histogram <- P1.dat %>%
  mutate(Color = factor(Color, levels=names(ColorKey))) %>%
  ggplot(aes(x=log2(ECRatio.ComparedToGenomewideMedian_Branaplam.Risdiplam), fill=Color)) +
  geom_histogram() +
  scale_fill_manual(values=ColorKey) +
  theme(legend.position="bottom") +
  labs(x="Specificity\nlog2(BranaplamEC50/RisdiplamEC50)", caption="EC50s normalized to median before comparing ratios")

P.histogram

Version Author Date
a97c2eb Benjmain Fair 2023-03-08

Let’s just consider stronger specificity effects, with abs(log2 ratio) > 2.

Note that I haven’t filtered these GA-GT intron affects for those with effects on the host gene. So like some of these hits may not effect host gene expression.

P.histogram +
  xlim(c(-10, 10)) +
  geom_vline(xintercept=c(-2,2))

Version Author Date
a97c2eb Benjmain Fair 2023-03-08 
SpecificEffects <- P1.dat %>%
  mutate(log2Ratio = log2(ECRatio.ComparedToGenomewideMedian_Branaplam.Risdiplam)) %>%
  filter(abs(log2Ratio)>2) %>%
  filter(!Color == "Non significant") %>%
  separate_rows(gene_names, gene_ids, sep=',') %>%
  dplyr::select(Color, log2Ratio, gene_names, gene_ids) %>%
  # filter(gene_names == "HTT") %>%
  arrange(log2Ratio)

head(SpecificEffects)
# A tibble: 6 × 4
  Color              log2Ratio gene_names gene_ids          
  <chr>                  <dbl> <chr>      <chr>             
1 Branaplam-specific    -30.8  ASB3       ENSG00000115239.24
2 Branaplam-specific    -15.7  SETD5      ENSG00000168137.18
3 Branaplam-specific     -8.13 C8orf88    ENSG00000253250.3 
4 Branaplam-specific     -7.52 NCF2       ENSG00000116701.15
5 Branaplam-specific     -7.16 ANKRD10    ENSG00000088448.14
6 Branaplam-specific     -6.67 DAPP1      ENSG00000070190.13

Let’s also merge this with some gene expression data. For convenience, I’m just going to merge it with the spearman of the dose:response effect on expression, and also gather the FoldChange at the mid-point dose, which I think is a reasonable place to start to see some effects (the high dose in this experiment might be a bit ridiculous from the pharmacology point of view).

gene_names <- read_tsv("../data/Genes.list.txt")

GeneExpressionData.tidy <- read_tsv("../code/DoseResponseData/LCL/TidyExpressionDoseData.txt.gz") %>%
  mutate(CPM = log2(CPM)) %>%
  mutate(ensembl_gene_id = str_replace(Geneid, "^(.+?)\\..*$", "\\1")) %>%
  left_join(gene_names)

As a data parsing side note… I can quickly get the doses for the midpoint doses by filtering on the color column. Let me remember how I coded the colors to get the appropriate doses

GeneExpressionData.tidy %>%
  distinct(color, .keep_all=T) %>%
  ggplot(aes(x=1, y=color, fill=color)) +
  geom_raster() +
  geom_label(aes(label=color)) +
  scale_fill_identity() +
  facet_wrap(~treatment)

Version Author Date
a97c2eb Benjmain Fair 2023-03-08

Ok, so in other words, I want to filter for color = #969696 (DMSO), or #74C476 (middle branaplam dose), or #6BAED6 (middle risdiplam dose).

MoleculeSpecificEffects <- GeneExpressionData.tidy %>%
  filter(color %in% c("#74C476", "#6BAED6")) %>%
  filter(Geneid %in% SpecificEffects$gene_ids) %>%
  dplyr::select(Geneid, CPM, SampleName, treatment, spearman) %>%
  left_join(
    GeneExpressionData.tidy %>%
      filter(color %in% c("#969696")) %>%
      filter(Geneid %in% SpecificEffects$gene_ids) %>%
      dplyr::select(Geneid, CPM) %>%
      group_by(Geneid) %>%
      summarise(CPM_DMSO = mean(CPM))
  ) %>%
  mutate(Log2FoldChange = CPM - CPM_DMSO) %>%
  pivot_wider(names_from = c("treatment"), values_from=c("CPM", "spearman", "SampleName", "Log2FoldChange")) %>%
  inner_join(SpecificEffects, by=c("Geneid"="gene_ids")) %>%
  dplyr::rename(log2RatioOfSplicingED50= log2Ratio) %>%
  arrange(log2RatioOfSplicingED50)

MoleculeSpecificEffects %>%
  filter(gene_names == "HTT") %>%
  knitr::kable()
Geneid CPM_DMSO CPM_Branaplam CPM_Risdiplam spearman_Branaplam spearman_Risdiplam SampleName_Branaplam SampleName_Risdiplam Log2FoldChange_Branaplam Log2FoldChange_Risdiplam Color log2RatioOfSplicingED50 gene_names
ENSG00000197386.12 7.892803 4.910666 7.186162 -0.8333333 -0.8666667 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 -2.982138 -0.7066416 Branaplam-specific -3.346746 HTT

Ok that makes sense. Note that the CPM columns are actually log2CPM. We can see that HTT has a branaplam-specific effect.

Let’s print the full table…

So for this table I only considered ones where there is a significant difference in splicing ED50 between risdiplam and branaplam. Technically, I renormalized EC50 to the genomewide median ED50 for GA-GT introns since risdiplam and branaplam molar concentrations aren’t directly comparable even for STAT1. Then, I filtered for the stronger effects (with log2 ratio) magnitude greater than 2 (see histogram above with the 2 vertical lines). Then, in the table, I am also listing the effects on expression in branaplam and risdiplam… Because some of the GA-GT introns that get upregulated don’t actually effect host gene expression, like if they don’t create an NMD poison exon. Note that all the CPM values in the table are actually log2CPM.

Then, in the table there is also a column called “spearman_Branaplam” or “spearman_Risdiplam” which is the expression dose:response speraman correlation across the whole titration series… so in the example of HTT which is on the table (and also above), you can see that there is a similarly strong negative spearman correlation coefficient in the risdiplam and branaplam, but the log2RatioOfSplicingED50 column suggests it is quite branaplam specific, and if you look at the CPM_Branaplam (100nM branaplam) column compared to the CPM_DMSO and CPM_Risdiplam (316nM risdiplam) columns, you can see that there really is only a strong expression effect in branaplam at that midpoint dose… The midpoint dose is roughly comparable to some of the relatively low doses that Jingxin used in the recent experiment of 52 molecules

MoleculeSpecificEffects %>%
  knitr::kable()
Geneid CPM_DMSO CPM_Branaplam CPM_Risdiplam spearman_Branaplam spearman_Risdiplam SampleName_Branaplam SampleName_Risdiplam Log2FoldChange_Branaplam Log2FoldChange_Risdiplam Color log2RatioOfSplicingED50 gene_names
ENSG00000115239.24 4.3081982 4.1890728 4.3196633 -0.8166667 0.9000000 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 -0.1191255 0.0114651 Branaplam-specific -30.837171 ASB3
ENSG00000168137.18 6.0661148 6.1276722 6.2102237 -0.0166667 0.6333333 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 0.0615575 0.1441089 Branaplam-specific -15.702685 SETD5
ENSG00000253250.3 2.5719371 2.9005508 2.5340035 0.8000000 0.5166667 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 0.3286136 -0.0379337 Branaplam-specific -8.128940 C8orf88
ENSG00000116701.15 5.4192338 5.5229186 5.5790500 -0.4000000 -0.1333333 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 0.1036848 0.1598162 Branaplam-specific -7.515091 NCF2
ENSG00000088448.14 5.0353703 4.9280315 5.1069535 -0.2500000 0.8000000 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 -0.1073388 0.0715831 Branaplam-specific -7.155299 ANKRD10
ENSG00000070190.13 5.7763049 4.3533232 3.9080913 -0.9666667 -0.9000000 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 -1.4229816 -1.8682135 Branaplam-specific -6.669824 DAPP1
ENSG00000050748.17 5.2584252 5.3263748 5.3632371 -0.5333333 0.5500000 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 0.0679496 0.1048119 Branaplam-specific -6.577305 MAPK9
ENSG00000196792.12 3.7273769 4.0174847 4.0453431 0.9500000 0.8833333 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 0.2901078 0.3179662 Branaplam-specific -6.202894 STRN3
ENSG00000170476.16 8.4436025 8.1173012 8.3130122 -1.0000000 -0.9500000 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 -0.3263014 -0.1305903 Branaplam-specific -5.524008 MZB1
ENSG00000064933.18 4.6810344 2.2646238 2.5518368 -0.9833333 -1.0000000 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 -2.4164106 -2.1291976 Branaplam-specific -5.522988 PMS1
ENSG00000087460.25 10.2499857 10.0997995 10.1649180 -0.9333333 -0.9166667 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 -0.1501862 -0.0850677 Branaplam-specific -4.958655 GNAS
ENSG00000180198.16 7.0524244 9.6440401 7.2642243 0.9833333 0.9166667 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 2.5916157 0.2117999 Branaplam-specific -4.819941 RCC1
ENSG00000242125.3 4.9287895 3.2413027 4.5095734 -0.9833333 -0.9666667 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 -1.6874868 -0.4192161 Branaplam-specific -4.819941 SNHG3
ENSG00000112739.17 6.2932301 6.3802628 6.2891568 -0.4333333 -0.2333333 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 0.0870327 -0.0040733 Branaplam-specific -4.813817 PRPF4B
ENSG00000205861.12 0.9774978 1.2937702 0.5553771 -0.1833333 -0.7500000 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 0.3162724 -0.4221207 Branaplam-specific -4.744962 PCOTH
ENSG00000131981.16 5.6559843 5.6235088 5.7031307 0.2333333 0.7666667 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 -0.0324755 0.0471464 Branaplam-specific -4.693610 LGALS3
ENSG00000078747.15 6.1734625 5.7468173 6.1599485 -0.8666667 -0.6666667 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 -0.4266451 -0.0135139 Branaplam-specific -4.614054 ITCH
ENSG00000258724.1 -Inf -6.1574410 -Inf -0.1980295 0.2738613 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 Inf NaN Branaplam-specific -4.608812 PINX1
ENSG00000254093.9 3.9415665 3.6255573 3.8983668 -0.9833333 -0.6666667 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 -0.3160092 -0.0431996 Branaplam-specific -4.608812 PINX1
ENSG00000143569.19 7.7077429 7.2917077 7.0205834 -0.8666667 -0.9500000 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 -0.4160352 -0.6871596 Branaplam-specific -4.586487 UBAP2L
ENSG00000102921.8 6.1395411 6.0907831 6.1279102 0.3666667 0.0666667 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 -0.0487579 -0.0116309 Branaplam-specific -4.428989 N4BP1
ENSG00000100368.14 3.9931320 3.6222784 3.9677602 -0.9000000 -0.3833333 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 -0.3708536 -0.0253719 Branaplam-specific -4.411832 CSF2RB
ENSG00000111237.19 5.9226823 5.1901804 5.8840081 -1.0000000 -0.6666667 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 -0.7325019 -0.0386742 Branaplam-specific -4.338322 VPS29
ENSG00000172175.15 6.6152367 6.9167005 6.7481051 -0.1333333 0.3833333 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 0.3014638 0.1328683 Branaplam-specific -4.278250 MALT1
ENSG00000010270.13 5.7620132 5.9089846 5.7548570 0.9333333 0.8333333 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 0.1469714 -0.0071562 Branaplam-specific -4.248940 STARD3NL
ENSG00000090372.15 6.0804844 4.2989135 5.9960428 -0.9333333 -0.9333333 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 -1.7815710 -0.0844417 Branaplam-specific -4.199628 STRN4
ENSG00000176531.10 3.2001713 3.5516429 3.4279162 0.9000000 0.8000000 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 0.3514715 0.2277449 Branaplam-specific -4.154132 PHLDB3
ENSG00000000938.13 5.4668031 4.9207099 5.3181011 -0.5000000 -0.5000000 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 -0.5460932 -0.1487020 Branaplam-specific -3.990816 FGR
ENSG00000147905.17 5.5084515 5.2834282 5.3798056 -0.9500000 -0.2333333 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 -0.2250233 -0.1286460 Branaplam-specific -3.951407 ZCCHC7
ENSG00000153914.16 5.3992150 3.2561870 5.0795539 -0.8833333 -0.7833333 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 -2.1430280 -0.3196611 Branaplam-specific -3.940037 SREK1
ENSG00000101639.18 4.9258571 5.0520124 4.8327785 -0.4333333 -0.8166667 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 0.1261553 -0.0930786 Branaplam-specific -3.934320 CEP192
ENSG00000163960.12 5.5158041 5.7377556 5.6465791 0.5666667 0.4500000 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 0.2219515 0.1307750 Branaplam-specific -3.872331 UBXN7
ENSG00000106268.15 4.9628095 5.2294993 5.0653461 0.4833333 -0.5833333 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 0.2666898 0.1025367 Branaplam-specific -3.805406 NUDT1
ENSG00000102897.10 4.5736910 4.8807922 4.4613245 -0.1500000 -0.0500000 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 0.3071011 -0.1123666 Branaplam-specific -3.734031 LYRM1
ENSG00000110090.13 4.8495802 5.0761787 4.9416200 0.4666667 0.5333333 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 0.2265985 0.0920398 Branaplam-specific -3.648140 CPT1A
ENSG00000134882.16 5.1507778 4.3414083 4.6378393 -0.9833333 -0.9833333 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 -0.8093695 -0.5129385 Branaplam-specific -3.622183 UBAC2
ENSG00000171530.14 6.4857089 5.8097853 6.3302933 -0.9666667 -0.3833333 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 -0.6759236 -0.1554156 Branaplam-specific -3.621325 TBCA
ENSG00000147439.13 5.1520551 5.0749800 5.0856878 -0.8833333 0.4833333 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 -0.0770752 -0.0663673 Branaplam-specific -3.615688 BIN3
ENSG00000177565.18 5.4283368 5.4312737 5.6095957 -0.6666667 0.1000000 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 0.0029369 0.1812589 Branaplam-specific -3.588868 TBL1XR1
ENSG00000064313.12 5.4381021 4.5840260 4.3196633 -0.8000000 -0.9000000 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 -0.8540761 -1.1184388 Branaplam-specific -3.576764 TAF2
ENSG00000167491.17 6.6118164 6.6513222 6.6583563 -0.7166667 0.5000000 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 0.0395057 0.0465399 Branaplam-specific -3.547966 GATAD2A
ENSG00000080845.17 5.4454116 5.1250684 5.3773074 -0.8166667 0.6333333 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 -0.3203432 -0.0681042 Branaplam-specific -3.535267 DLGAP4
ENSG00000151233.11 4.0694815 2.9167005 4.0148087 -0.9833333 -0.6833333 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 -1.1527810 -0.0546728 Branaplam-specific -3.487187 GXYLT1
ENSG00000139116.18 5.2251809 5.1192651 5.2784859 -0.7166667 -0.5000000 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 -0.1059158 0.0533050 Branaplam-specific -3.473336 KIF21A
ENSG00000108061.12 6.6597812 6.5503495 6.4688532 0.0666667 -0.6333333 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 -0.1094316 -0.1909279 Branaplam-specific -3.422720 SHOC2
ENSG00000130475.14 4.8883909 4.7554484 4.8709263 -0.9833333 0.3000000 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 -0.1329426 -0.0174646 Branaplam-specific -3.391896 FCHO1
ENSG00000166974.13 5.3297854 5.5865519 5.3556426 -0.1500000 0.9333333 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 0.2567665 0.0258572 Branaplam-specific -3.348989 MAPRE2
ENSG00000197386.12 7.8928033 4.9106655 7.1861617 -0.8333333 -0.8666667 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 -2.9821378 -0.7066416 Branaplam-specific -3.346746 HTT
ENSG00000127554.13 4.9440687 4.9929410 5.0031897 -0.6666667 0.3333333 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 0.0488723 0.0591209 Branaplam-specific -3.315597 GFER
ENSG00000180901.11 5.3235507 5.3641595 5.4693225 -0.5833333 0.4500000 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 0.0406088 0.1457718 Branaplam-specific -3.297018 KCTD2
ENSG00000132394.11 4.7444785 4.9776270 4.9637693 -0.4666667 -0.2333333 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 0.2331485 0.2192908 Branaplam-specific -3.285066 EEFSEC
ENSG00000107263.18 7.7481426 8.0216906 7.9613528 0.9333333 0.9666667 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 0.2735480 0.2132102 Branaplam-specific -3.249082 RAPGEF1
ENSG00000135363.12 4.3804710 4.2886085 4.4726029 -0.3166667 -0.0333333 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 -0.0918626 0.0921319 Branaplam-specific -3.238942 LMO2
ENSG00000156642.17 5.8592412 5.9143566 5.8188535 0.9000000 0.6333333 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 0.0551154 -0.0403877 Branaplam-specific -3.206832 NPTN
ENSG00000126581.13 6.1942389 6.4487329 6.2525491 0.9833333 0.8333333 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 0.2544940 0.0583102 Branaplam-specific -3.183238 BECN1
ENSG00000196843.17 6.6276038 6.6349427 6.6359595 -0.0166667 0.5666667 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 0.0073389 0.0083558 Branaplam-specific -3.167849 ARID5A
ENSG00000285733.1 -Inf -Inf -Inf NA NA Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 NaN NaN Branaplam-specific -3.141239 AL031315.1
ENSG00000184465.16 0.7275724 0.3019907 0.5411633 -0.9166667 -0.5166667 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 -0.4255818 -0.1864092 Branaplam-specific -3.141239 WDR27
ENSG00000100281.14 4.6081853 4.5670729 4.6862757 -0.8333333 -0.6000000 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 -0.0411124 0.0780904 Branaplam-specific -3.090861 HMGXB4
ENSG00000136732.16 6.2975339 6.1987359 6.1234450 -0.8500000 -0.6333333 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 -0.0987981 -0.1740889 Branaplam-specific -3.077982 GYPC
ENSG00000126773.13 5.1310343 5.0410041 5.1415178 0.1500000 0.6833333 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 -0.0900302 0.0104835 Branaplam-specific -3.013184 PCNX4
ENSG00000204899.6 4.8599721 4.6337219 4.8393279 -0.9333333 -0.6833333 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 -0.2262502 -0.0206442 Branaplam-specific -2.991499 MZT1
ENSG00000163875.15 5.4606052 5.3918624 5.4006212 -0.7833333 0.4833333 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 -0.0687428 -0.0599841 Branaplam-specific -2.964981 MEAF6
ENSG00000116473.14 7.6765605 7.6621394 7.5971973 -0.4500000 -0.8666667 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 -0.0144210 -0.0793632 Branaplam-specific -2.956315 RAP1A
ENSG00000106028.11 6.8109793 6.0446829 6.7728359 -0.9333333 -0.7500000 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 -0.7662965 -0.0381434 Branaplam-specific -2.948600 SSBP1
ENSG00000278845.5 6.0796456 6.1821307 6.0841568 0.9166667 0.7333333 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 0.1024851 0.0045111 Branaplam-specific -2.928467 MRPL45
ENSG00000175220.12 5.6868493 5.2503579 5.8641623 -0.9000000 -0.2333333 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 -0.4364914 0.1773130 Branaplam-specific -2.885549 ARHGAP1
ENSG00000113558.19 6.6634023 6.1644871 6.6943265 -0.9833333 -0.3333333 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 -0.4989152 0.0309241 Branaplam-specific -2.876467 SKP1
ENSG00000272772.1 -Inf -Inf -5.1170482 -0.1369306 -0.1460593 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 NaN Inf Branaplam-specific -2.876467 AC104109.3
ENSG00000138185.20 7.9964849 8.0054224 7.9008041 -0.3500000 -0.1500000 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 0.0089375 -0.0956808 Branaplam-specific -2.835834 ENTPD1
ENSG00000118454.13 4.8260367 4.6132229 4.9503861 -0.9500000 0.1166667 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 -0.2128138 0.1243494 Branaplam-specific -2.807663 ANKRD13C
ENSG00000165025.15 7.1479192 7.0854354 7.2593485 -0.9666667 -0.5333333 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 -0.0624838 0.1114293 Branaplam-specific -2.803027 SYK
ENSG00000126653.18 4.8593663 4.6659263 4.8114702 -0.9166667 0.4333333 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 -0.1934400 -0.0478962 Branaplam-specific -2.798763 NSRP1
ENSG00000065150.21 8.3889944 8.4050400 8.3266076 -0.4166667 -0.7166667 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 0.0160456 -0.0623867 Branaplam-specific -2.785237 IPO5
ENSG00000197150.12 5.2848027 4.8937680 5.4143332 -0.9000000 0.4500000 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 -0.3910347 0.1295306 Branaplam-specific -2.783347 ABCB8
ENSG00000117419.16 5.3792829 5.4247010 5.3474975 -0.6666667 0.1333333 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 0.0454181 -0.0317854 Branaplam-specific -2.762573 ERI3
ENSG00000112851.14 6.0480217 5.4251715 6.1021203 -0.9333333 -0.5666667 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 -0.6228502 0.0540986 Branaplam-specific -2.751726 ERBIN
ENSG00000139826.6 4.2150254 4.1879643 4.1141730 -0.6833333 0.5166667 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 -0.0270611 -0.1008524 Branaplam-specific -2.748700 ABHD13
ENSG00000130818.12 2.6949993 3.2434385 2.7686482 0.9166667 0.7500000 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 0.5484392 0.0736488 Branaplam-specific -2.737337 ZNF426
ENSG00000197329.12 5.5318634 5.2577726 5.5384825 0.1500000 0.6000000 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 -0.2740908 0.0066191 Branaplam-specific -2.734313 PELI1
ENSG00000170627.11 4.8964738 3.2305763 4.4556520 -0.9500000 -0.9833333 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 -1.6658975 -0.4408218 Branaplam-specific -2.732351 GTSF1
ENSG00000125991.19 7.2077209 7.2163758 7.2842313 0.6500000 0.8000000 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 0.0086549 0.0765104 Branaplam-specific -2.711622 ERGIC3
ENSG00000070882.13 5.1022965 5.0162362 5.1308793 -0.8500000 -0.2833333 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 -0.0860603 0.0285828 Branaplam-specific -2.711605 OSBPL3
ENSG00000082146.13 4.0901520 4.3404109 4.0850756 -0.4166667 0.2666667 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 0.2502589 -0.0050764 Branaplam-specific -2.667072 STRADB
ENSG00000101109.12 7.1971436 7.1499022 7.3565307 0.2500000 0.5166667 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 -0.0472414 0.1593871 Branaplam-specific -2.666348 STK4
ENSG00000197150.12 5.2848027 4.8937680 5.4143332 -0.9000000 0.4500000 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 -0.3910347 0.1295306 Branaplam-specific -2.631513 ABCB8
ENSG00000100852.13 4.0440229 4.2487641 4.1201617 0.2333333 0.4666667 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 0.2047412 0.0761389 Branaplam-specific -2.547770 ARHGAP5
ENSG00000060237.17 7.9237791 8.0417713 8.0869115 -0.7166667 0.5333333 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 0.1179922 0.1631324 Branaplam-specific -2.540969 WNK1
ENSG00000180628.15 6.6337140 6.8000239 6.7122781 0.8333333 0.6166667 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 0.1663099 0.0785641 Branaplam-specific -2.539899 PCGF5
ENSG00000120029.13 5.5698725 5.5713299 5.5790500 -0.6833333 -0.5833333 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 0.0014574 0.0091774 Branaplam-specific -2.535363 ARMH3
ENSG00000185009.12 6.6747080 6.5325570 6.6298859 -0.9000000 -0.5833333 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 -0.1421510 -0.0448221 Branaplam-specific -2.534809 AP3M1
ENSG00000106785.15 6.4574479 6.0040059 6.5449525 -0.9833333 0.8333333 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 -0.4534420 0.0875046 Branaplam-specific -2.522308 TRIM14
ENSG00000170627.11 4.8964738 3.2305763 4.4556520 -0.9500000 -0.9833333 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 -1.6658975 -0.4408218 Branaplam-specific -2.494362 GTSF1
ENSG00000182628.13 5.7537633 5.5277458 5.6078919 -0.9500000 -0.8333333 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 -0.2260175 -0.1458715 Branaplam-specific -2.465174 SKA2
ENSG00000116212.15 5.1342687 5.1684266 5.0390349 -0.5500000 -0.8833333 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 0.0341580 -0.0952338 Branaplam-specific -2.458638 LRRC42
ENSG00000154511.12 5.1856157 4.8842182 4.9868952 -0.9833333 -0.7666667 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 -0.3013975 -0.1987205 Branaplam-specific -2.435005 DIPK1A
ENSG00000100979.15 3.3330269 3.2667253 3.2048799 0.6500000 0.7833333 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 -0.0663015 -0.1281470 Branaplam-specific -2.429309 PLTP
ENSG00000116353.16 4.2802065 3.8425590 4.3165372 -0.9000000 -0.7333333 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 -0.4376474 0.0363308 Branaplam-specific -2.415464 MECR
ENSG00000165406.16 5.7545129 5.8927478 5.8762440 -0.4333333 0.8833333 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 0.1382349 0.1217311 Branaplam-specific -2.413024 MARCHF8
ENSG00000115084.15 3.5913508 3.6849094 3.9341607 -0.6166667 0.4666667 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 0.0935586 0.3428099 Branaplam-specific -2.381796 SLC35F5
ENSG00000178397.13 -Inf -Inf -6.1170482 NA 0.1369306 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 NaN Inf Branaplam-specific -2.381226 FAM220A
ENSG00000286075.1 -Inf -Inf -Inf NA NA Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 NaN NaN Branaplam-specific -2.381226 AC009412.1
ENSG00000147586.10 3.5316801 3.5007705 3.6692214 -0.7833333 -0.8500000 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 -0.0309096 0.1375413 Branaplam-specific -2.342313 MRPS28
ENSG00000276418.5 -Inf -6.1574410 -4.5320857 0.4500000 -0.3333333 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 Inf Inf Branaplam-specific -2.342313 AC036214.3
ENSG00000196295.12 1.7486049 1.5840260 1.9436477 -0.9333333 0.3166667 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 -0.1645788 0.1950428 Branaplam-specific -2.297164 GARS1-DT
ENSG00000008018.9 7.6105603 6.4940593 7.6388825 -0.9833333 -0.1833333 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 -1.1165011 0.0283222 Branaplam-specific -2.280594 PSMB1
ENSG00000181007.9 1.9254386 2.3019907 1.8885763 0.8500000 0.8833333 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 0.3765520 -0.0368623 Branaplam-specific -2.275857 ZFP82
ENSG00000064652.11 1.3213263 1.2604116 1.4679143 -0.4333333 0.1500000 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 -0.0609147 0.1465880 Branaplam-specific -2.260318 SNX24
ENSG00000141068.14 5.8475194 6.0987677 6.1482738 0.6333333 0.3666667 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 0.2512483 0.3007544 Branaplam-specific -2.252941 KSR1
ENSG00000189091.13 8.2776651 8.4029516 8.4148206 -0.5666667 -0.4666667 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 0.1252865 0.1371555 Branaplam-specific -2.251402 SF3B3
ENSG00000026751.17 9.1854115 8.8787327 8.9119370 -0.3000000 -0.0666667 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 -0.3066789 -0.2734746 Branaplam-specific -2.246607 SLAMF7
ENSG00000122359.18 7.6498416 6.2896423 7.5345641 -0.9666667 -0.9500000 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 -1.3601993 -0.1152775 Branaplam-specific -2.240435 ANXA11
ENSG00000113719.16 7.5501986 7.5005473 7.4881998 -0.8500000 0.1333333 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 -0.0496512 -0.0619988 Branaplam-specific -2.233131 ERGIC1
ENSG00000138698.15 5.4944600 5.6202253 5.4513830 -0.4500000 -0.7833333 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 0.1257654 -0.0430770 Branaplam-specific -2.220561 RAP1GDS1
ENSG00000189091.13 8.2776651 8.4029516 8.4148206 -0.5666667 -0.4666667 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 0.1252865 0.1371555 Branaplam-specific -2.209715 SF3B3
ENSG00000115840.14 5.5526802 4.9379561 5.5799193 -0.9666667 -0.1500000 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 -0.6147242 0.0272391 Branaplam-specific -2.208868 SLC25A12
ENSG00000167081.18 5.3475640 5.0713777 5.1798680 -0.9000000 -0.9166667 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 -0.2761862 -0.1676960 Branaplam-specific -2.197212 PBX3
ENSG00000180104.16 6.1625641 5.4927133 6.2344432 -0.9500000 -0.6666667 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 -0.6698509 0.0718791 Branaplam-specific -2.126322 EXOC3
ENSG00000134852.15 3.8881696 3.8964849 3.8645191 0.0000000 0.7166667 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 0.0083154 -0.0236505 Branaplam-specific -2.103294 CLOCK
ENSG00000117228.10 6.2113174 6.2289605 6.3039646 0.8000000 0.8833333 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 0.0176431 0.0926473 Branaplam-specific -2.094464 GBP1
ENSG00000176407.18 6.1227173 6.0800678 6.1379803 0.7166667 0.3666667 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 -0.0426495 0.0152631 Branaplam-specific -2.092161 KCMF1
ENSG00000142634.13 7.3680775 7.5167513 7.6087441 -0.3333333 0.0500000 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 0.1486738 0.2406666 Branaplam-specific -2.086733 EFHD2
ENSG00000234127.9 7.2326711 7.3382892 7.3170587 1.0000000 0.9833333 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 0.1056181 0.0843876 Branaplam-specific -2.073400 TRIM26
ENSG00000047621.12 4.8199364 4.5481914 4.6830428 -0.9166667 -0.7166667 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 -0.2717450 -0.1368936 Branaplam-specific -2.068560 C12orf4
ENSG00000146757.14 4.2245115 4.2927393 3.9940874 0.7000000 0.0333333 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 0.0682278 -0.2304240 Branaplam-specific -2.056343 ZNF92
ENSG00000172469.16 5.5678175 5.2116111 5.3902509 -0.9500000 -0.7333333 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 -0.3562064 -0.1775666 Branaplam-specific -2.016101 MANEA
ENSG00000185920.16 4.0582967 4.2551289 4.3434077 0.3000000 0.6333333 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 0.1968322 0.2851109 Branaplam-specific -2.013666 PTCH1
ENSG00000107290.14 7.5088671 7.1713742 7.3764320 -0.7500000 -0.7333333 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 -0.3374929 -0.1324351 Risdiplam-specific 2.002415 SETX
ENSG00000123983.14 6.1040022 5.9942099 6.0578775 -0.8666667 -0.7333333 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 -0.1097923 -0.0461247 Risdiplam-specific 2.015697 ACSL3
ENSG00000115415.20 10.4388782 9.7878886 9.3949206 -0.9333333 -0.9500000 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 -0.6509896 -1.0439576 Risdiplam-specific 2.103504 STAT1
ENSG00000100485.12 4.5238499 4.4526611 3.8429537 -0.7833333 -0.8500000 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 -0.0711888 -0.6808962 Risdiplam-specific 2.140344 SOS2
ENSG00000160293.17 6.2055016 6.4723710 6.3220035 -0.2333333 -0.1500000 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 0.2668694 0.1165019 Risdiplam-specific 2.252860 VAV2
ENSG00000256646.7 -Inf -Inf -Inf -0.2738613 -0.2008316 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 NaN NaN Risdiplam-specific 2.261174 AC010132.3
ENSG00000106588.11 2.4132009 2.1824090 2.4939766 0.4333333 -0.0500000 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 -0.2307919 0.0807756 Risdiplam-specific 2.261174 PSMA2
ENSG00000119402.17 5.7926703 6.0848400 5.9770295 0.5666667 -0.2833333 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 0.2921696 0.1843591 Risdiplam-specific 2.268566 FBXW2
ENSG00000112701.18 5.8132585 5.3349130 5.5127637 -0.9500000 -0.9333333 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 -0.4783455 -0.3004948 Risdiplam-specific 2.295573 SENP6
ENSG00000069869.16 3.7094266 3.9353162 3.7883388 -0.3166667 0.6666667 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 0.2258896 0.0789122 Risdiplam-specific 2.300576 NEDD4
ENSG00000205268.11 5.8922730 5.9970607 6.0890504 -0.4833333 -0.0166667 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 0.1047877 0.1967774 Risdiplam-specific 2.306077 PDE7A
ENSG00000164292.13 3.9204349 3.7821383 4.1821598 -0.8833333 0.8166667 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 -0.1382966 0.2617249 Risdiplam-specific 2.311566 RHOBTB3
ENSG00000107263.18 7.7481426 8.0216906 7.9613528 0.9333333 0.9666667 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 0.2735480 0.2132102 Risdiplam-specific 2.345126 RAPGEF1
ENSG00000270800.3 -Inf -6.1574410 -6.1170482 0.3729349 0.3168472 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 Inf Inf Risdiplam-specific 2.378209 RPS10-NUDT3
ENSG00000124614.16 0.0729407 0.6627380 1.0225031 0.9500000 0.7833333 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 0.5897973 0.9495625 Risdiplam-specific 2.378209 RPS10
ENSG00000268790.5 -Inf -Inf -6.1170482 0.4107919 -0.4016632 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 NaN Inf Risdiplam-specific 2.396985 AC008764.4
ENSG00000214046.8 4.3300136 4.2001110 4.2547284 -0.8000000 -0.9333333 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 -0.1299025 -0.0752851 Risdiplam-specific 2.396985 SMIM7
ENSG00000079616.13 6.7642928 6.9745767 6.8425915 -0.3166667 -0.5666667 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 0.2102839 0.0782987 Risdiplam-specific 2.405511 KIF22
ENSG00000188822.8 4.6854929 4.5525044 4.6168145 -0.9833333 -0.1666667 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 -0.1329885 -0.0686784 Risdiplam-specific 2.471255 CNR2
ENSG00000158234.12 2.8341480 2.8924076 2.5729498 -0.2166667 -0.9833333 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 0.0582596 -0.2611982 Risdiplam-specific 2.506330 FAIM
ENSG00000071189.21 4.3992788 4.0857330 4.3351930 -0.9666667 -0.7333333 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 -0.3135458 -0.0640858 Risdiplam-specific 2.546995 SNX13
ENSG00000092931.11 3.8965532 3.4124147 3.6724854 -0.9333333 -0.8500000 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 -0.4841385 -0.2240678 Risdiplam-specific 2.582979 MFSD11
ENSG00000168310.11 6.6533029 6.5494867 6.4978916 -0.8833333 -0.7000000 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 -0.1038163 -0.1554113 Risdiplam-specific 2.602322 IRF2
ENSG00000082996.19 5.1709045 5.2782293 5.0628609 0.7500000 0.6000000 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 0.1073248 -0.1080436 Risdiplam-specific 2.641557 RNF13
ENSG00000135974.10 4.1454717 3.9510835 3.9637693 -0.1500000 0.1166667 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 -0.1943882 -0.1817024 Risdiplam-specific 2.664718 C2orf49
ENSG00000056097.16 6.9803244 7.1103699 7.0268133 0.9000000 0.6666667 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 0.1300455 0.0464889 Risdiplam-specific 2.686858 ZFR
ENSG00000144228.9 4.6371177 4.5966114 4.5703275 0.1000000 -0.4666667 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 -0.0405063 -0.0667902 Risdiplam-specific 2.688732 SPOPL
ENSG00000197694.18 7.6158650 7.8129346 7.7234347 -0.6166667 -0.5833333 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 0.1970696 0.1075697 Risdiplam-specific 2.736914 SPTAN1
ENSG00000164187.7 4.0948224 4.2034061 4.0641040 -0.6500000 -0.5833333 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 0.1085837 -0.0307184 Risdiplam-specific 2.767962 LMBRD2
ENSG00000145725.19 5.7507703 5.5334301 5.6332400 -0.9833333 -0.7000000 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 -0.2173402 -0.1175302 Risdiplam-specific 2.820051 PPIP5K2
ENSG00000126457.21 8.9964156 8.8587580 8.5408842 -0.8166667 -1.0000000 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 -0.1376577 -0.4555314 Risdiplam-specific 2.855578 PRMT1
ENSG00000115942.9 4.9500416 4.9086482 4.8085062 -0.8666667 -0.8166667 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 -0.0413934 -0.1415354 Risdiplam-specific 2.872134 ORC2
ENSG00000108651.10 6.0570631 6.1625134 6.0820106 -0.5166667 -0.5000000 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 0.1054503 0.0249475 Risdiplam-specific 2.881977 UTP6
ENSG00000116095.11 4.1874370 4.3690583 4.3165372 0.9333333 0.9333333 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 0.1816212 0.1291002 Risdiplam-specific 2.906637 PLEKHA3
ENSG00000135677.11 6.2738119 6.4291643 6.2158275 -0.1833333 -0.7833333 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 0.1553524 -0.0579844 Risdiplam-specific 2.925520 GNS
ENSG00000153310.19 6.6416164 6.4650685 6.4758759 -0.8000000 -0.9333333 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 -0.1765479 -0.1657405 Risdiplam-specific 2.929163 CYRIB
ENSG00000123066.8 4.5321318 4.3865575 4.5013373 -0.9166667 -0.6000000 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 -0.1455743 -0.0307945 Risdiplam-specific 2.939934 MED13L
ENSG00000138413.13 6.0595426 5.9872173 5.4322551 -0.8333333 -0.9333333 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 -0.0723253 -0.6272874 Risdiplam-specific 2.944458 IDH1
ENSG00000145050.19 7.8300980 7.4571539 7.7066176 -0.9833333 -0.8833333 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 -0.3729441 -0.1234804 Risdiplam-specific 2.945036 MANF
ENSG00000083799.17 7.1540124 7.5326662 7.3407174 0.3333333 -0.2666667 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 0.3786538 0.1867050 Risdiplam-specific 2.991397 CYLD
ENSG00000093000.19 7.1753150 7.1531718 7.2170854 -0.2333333 -0.6166667 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 -0.0221432 0.0417704 Risdiplam-specific 3.049978 NUP50
ENSG00000047365.12 4.3533805 4.0299111 4.5141288 -0.2333333 -0.2166667 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 -0.3234694 0.1607483 Risdiplam-specific 3.070286 ARAP2
ENSG00000047056.16 4.0255494 4.0434576 4.1473944 -0.1000000 -0.0500000 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 0.0179083 0.1218450 Risdiplam-specific 3.072126 WDR37
ENSG00000011376.12 6.1420513 6.0806652 6.1177692 -0.7833333 -0.7333333 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 -0.0613861 -0.0242821 Risdiplam-specific 3.127228 LARS2
ENSG00000156469.9 4.4749894 4.2551289 3.9422962 -0.9000000 -0.9000000 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 -0.2198605 -0.5326931 Risdiplam-specific 3.137840 MTERF3
ENSG00000112308.13 6.6352683 6.7454994 6.9682582 -0.3166667 0.9166667 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 0.1102310 0.3329899 Risdiplam-specific 3.154727 C6orf62
ENSG00000105618.14 6.7530710 6.8565795 6.9014997 -0.4500000 -0.2500000 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 0.1035086 0.1484288 Risdiplam-specific 3.184942 PRPF31
ENSG00000196116.8 5.4226531 5.3981068 5.3892594 0.5666667 0.0500000 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 -0.0245462 -0.0333937 Risdiplam-specific 3.242623 TDRD7
ENSG00000139626.16 8.4289367 8.5256085 8.7186075 -0.6833333 0.6666667 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 0.0966718 0.2896708 Risdiplam-specific 3.271285 ITGB7
ENSG00000103415.12 6.1555052 6.0952245 6.0569412 -0.8833333 -0.9000000 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 -0.0602808 -0.0985641 Risdiplam-specific 3.271732 HMOX2
ENSG00000150753.12 8.7913377 8.8172873 8.7974771 -0.5333333 -0.7333333 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 0.0259496 0.0061394 Risdiplam-specific 3.275364 CCT5
ENSG00000137878.17 -Inf -Inf -Inf 0.2738613 0.7137184 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 NaN NaN Risdiplam-specific 3.275735 GCOM1
ENSG00000255529.9 3.6339962 3.9019035 3.7393773 0.9166667 0.6500000 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 0.2679073 0.1053811 Risdiplam-specific 3.275735 POLR2M
ENSG00000083720.13 6.8050233 6.7546999 6.7633006 -0.7666667 -0.7833333 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 -0.0503234 -0.0417228 Risdiplam-specific 3.340651 OXCT1
ENSG00000133028.12 5.8801698 4.4636952 4.9901689 -0.9833333 -0.9833333 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 -1.4164747 -0.8900010 Risdiplam-specific 3.386642 SCO1
ENSG00000110075.14 6.7897693 6.7616086 6.7692199 -0.6666667 -0.7000000 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 -0.0281607 -0.0205493 Risdiplam-specific 3.407066 PPP6R3
ENSG00000188352.12 6.2083491 6.1574255 5.8454863 -0.8500000 -0.8000000 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 -0.0509237 -0.3628628 Risdiplam-specific 3.444138 FOCAD
ENSG00000130159.14 5.6697893 5.7517021 5.7067171 0.2666667 0.0833333 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 0.0819128 0.0369277 Risdiplam-specific 3.486950 ECSIT
ENSG00000134779.15 6.4030684 6.4744182 6.2912815 -0.1500000 -0.7833333 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 0.0713497 -0.1117869 Risdiplam-specific 3.490282 TPGS2
ENSG00000136908.17 5.4260049 5.3142343 5.2907506 -0.9333333 -0.7166667 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 -0.1117706 -0.1352542 Risdiplam-specific 3.562029 DPM2
ENSG00000008083.14 4.9448917 5.3880062 5.1473944 -0.0833333 0.7833333 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 0.4431145 0.2025027 Risdiplam-specific 3.661699 JARID2
ENSG00000102900.13 6.5508501 6.7217594 6.6187193 -0.3500000 -0.5833333 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 0.1709092 0.0678692 Risdiplam-specific 3.739712 NUP93
ENSG00000235162.9 7.0215562 6.8476572 6.9780191 -0.9833333 -0.7333333 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 -0.1738990 -0.0435371 Risdiplam-specific 3.754437 C12orf75
ENSG00000146007.11 6.4357612 6.4700929 6.4008672 0.1166667 -0.7166667 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 0.0343318 -0.0348940 Risdiplam-specific 3.761796 ZMAT2
ENSG00000176142.13 4.9523113 4.8752936 5.0597483 -0.8000000 -0.4666667 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 -0.0770177 0.1074370 Risdiplam-specific 3.805878 TMEM39A
ENSG00000159063.13 5.2528677 5.1835218 4.7347008 -0.6666667 -0.9833333 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 -0.0693459 -0.5181668 Risdiplam-specific 3.900791 ALG8
ENSG00000168297.16 4.4572719 4.5264309 4.3976658 0.2000000 0.7000000 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 0.0691590 -0.0596060 Risdiplam-specific 3.991608 PXK
ENSG00000079332.15 5.9944819 5.9859423 5.9895147 -0.3333333 -0.7000000 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 -0.0085396 -0.0049672 Risdiplam-specific 4.011891 SAR1A
ENSG00000119414.11 6.3319944 6.1776702 6.1881585 -0.7833333 -0.9500000 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 -0.1543241 -0.1438358 Risdiplam-specific 4.112416 PPP6C
ENSG00000111252.11 6.0629114 6.0055797 6.3426395 -0.9333333 0.2333333 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 -0.0573317 0.2797282 Risdiplam-specific 4.151962 SH2B3
ENSG00000122643.22 6.6406786 6.5065618 6.6123601 0.6166667 0.6833333 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 -0.1341168 -0.0283185 Risdiplam-specific 4.343214 NT5C3A
ENSG00000283398.1 -4.7981191 -4.5724785 -5.1170482 0.3166667 0.2666667 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 0.2256407 -0.3189291 Risdiplam-specific 4.608559 AC114737.4
ENSG00000173064.13 5.4954924 5.4998775 5.7980842 -0.6666667 0.5833333 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 0.0043851 0.3025919 Risdiplam-specific 4.671793 HECTD4
ENSG00000177885.15 8.4372479 8.7097412 8.6053858 0.9000000 0.4666667 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 0.2724933 0.1681379 Risdiplam-specific 4.821942 GRB2
ENSG00000112159.12 5.4312820 5.2391638 5.5182164 -0.8166667 -0.6666667 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 -0.1921182 0.0869344 Risdiplam-specific 4.910025 MDN1
ENSG00000118596.12 3.7831582 3.8467795 3.7898424 -0.5833333 -0.6666667 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 0.0636214 0.0066842 Risdiplam-specific 4.924104 SLC16A7
ENSG00000149089.13 5.2158789 5.3263748 5.1213565 0.2833333 -0.8166667 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 0.1104960 -0.0945223 Risdiplam-specific 5.036669 APIP
ENSG00000135272.11 6.8400399 6.8520378 6.9162027 -0.1000000 0.2333333 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 0.0119979 0.0761628 Risdiplam-specific 5.123147 MDFIC
ENSG00000268746.1 -Inf -Inf -Inf -0.0456435 0.2738613 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 NaN NaN Risdiplam-specific 5.171954 AC010519.1
ENSG00000063169.10 4.1399160 4.3233492 4.1473944 0.3833333 -0.2000000 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 0.1834332 0.0074783 Risdiplam-specific 5.171954 BICRA
ENSG00000185019.17 3.5534098 3.6801870 3.6741147 -0.1166667 0.7333333 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 0.1267772 0.1207049 Risdiplam-specific 5.180906 UBOX5
ENSG00000215251.4 5.7330058 5.6710923 5.5575854 0.1000000 -0.9000000 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 -0.0619135 -0.1754204 Risdiplam-specific 5.180906 FASTKD5
ENSG00000138182.14 5.4225883 5.3488666 5.1992333 -0.7000000 -0.8166667 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 -0.0737216 -0.2233549 Risdiplam-specific 5.393297 KIF20B
ENSG00000184840.12 8.2545954 8.1966018 8.2168758 -0.9500000 -0.6500000 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 -0.0579936 -0.0377196 Risdiplam-specific 5.427913 TMED9
ENSG00000154001.14 6.1977946 6.3468815 6.2492740 0.7666667 0.6333333 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 0.1490869 0.0514794 Risdiplam-specific 5.687174 PPP2R5E
ENSG00000132383.12 7.7774015 7.6521262 7.6758437 -0.9833333 -0.8500000 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 -0.1252753 -0.1015578 Risdiplam-specific 6.285142 RPA1
ENSG00000102531.16 5.9941860 5.8126649 5.8716365 -0.9333333 -0.6666667 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 -0.1815210 -0.1225495 Risdiplam-specific 6.386338 FNDC3A
ENSG00000033030.15 4.4037055 4.4378165 3.6692214 0.6833333 -0.9833333 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 0.0341110 -0.7344841 Risdiplam-specific 6.732944 ZCCHC8
ENSG00000165806.21 4.8915779 4.7777241 4.7502305 -0.3333333 -0.8833333 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 -0.1138538 -0.1413474 Risdiplam-specific 6.869295 CASP7
ENSG00000155324.10 3.4373384 3.6499140 3.7174228 -0.2666667 0.2666667 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 0.2125755 0.2800844 Risdiplam-specific 7.089980 GRAMD2B
ENSG00000162694.14 3.5415857 3.6140485 3.0850756 0.6000000 -0.8000000 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 0.0724629 -0.4565101 Risdiplam-specific 7.608560 EXTL2
ENSG00000196422.11 2.3726765 2.4535838 2.6773676 0.5833333 0.3333333 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 0.0809074 0.3046912 Risdiplam-specific 10.672749 PPP1R26
ENSG00000243147.8 4.6638297 4.7517021 4.5141288 0.2833333 -0.7833333 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 0.0878724 -0.1497009 Risdiplam-specific 10.674744 MRPL33
ENSG00000163697.17 4.8469728 4.9073018 4.9280288 -0.5500000 -0.6333333 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 0.0603290 0.0810560 Risdiplam-specific 12.729286 APBB2
ENSG00000154864.12 6.4592491 5.8319535 6.0927097 -0.9000000 -0.8000000 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 -0.6272956 -0.3665394 Risdiplam-specific 15.785807 PIEZO2
ENSG00000070061.15 5.5878630 5.4909166 5.6378393 -0.7166667 -0.5333333 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 -0.0969464 0.0499763 Risdiplam-specific 17.798155 ELP1
ENSG00000165475.15 3.9468619 0.7373768 3.5375878 -0.9833333 -0.9333333 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 -3.2094851 -0.4092741 Risdiplam-specific 21.628056 CRYL1
ENSG00000144036.16 2.7150069 3.1486207 2.7347008 0.9833333 0.6666667 Branaplam_100_LCL_polyA_1 Risdiplam_316_LCL_polyA_1 0.4336138 0.0196939 Risdiplam-specific 24.606656 EXOC6B

Yang noted that we didn’t see FOXM1 or LRKK2 on this list. Let’s investigate why.

SpecificEffects %>%
  filter(gene_names %in% c("FOXM1", "LRRK2"))
# A tibble: 0 × 4
# … with 4 variables: Color <chr>, log2Ratio <dbl>, gene_names <chr>,
#   gene_ids <chr>
GA.GT.Introns %>%
  separate_rows(gene_names, gene_ids, sep=',') %>%
  filter(gene_names %in% c("FOXM1", "LRRK2", "HTT", "STAT1"))
# A tibble: 8 × 34
  junc        `#Chrom`  start    end gid   strand.y seq   Donor.score gene_names
  <chr>       <chr>     <dbl>  <dbl> <chr> <chr>    <chr>       <dbl> <chr>     
1 chr12:2859… chr12    2.86e6 2.86e6 chr1… -        ATGA…        4.63 FOXM1     
2 chr12:4022… chr12    4.02e7 4.02e7 chr1… +        ATGA…        3.60 LRRK2     
3 chr2:19097… chr2     1.91e8 1.91e8 chr2… -        AGGA…        4.83 STAT1     
4 chr2:19101… chr2     1.91e8 1.91e8 chr2… -        CAGA…        5.99 STAT1     
5 chr2:19101… chr2     1.91e8 1.91e8 chr2… -        CAGA…        5.99 STAT1     
6 chr4:32137… chr4     3.21e6 3.21e6 chr4… +        CAGA…        5.53 HTT       
7 chr4:32152… chr4     3.22e6 3.22e6 chr4… +        CAGA…        5.41 HTT       
8 chr4:32152… chr4     3.22e6 3.22e6 chr4… +        CAGA…        5.41 HTT       
# … with 25 more variables: gene_ids <chr>, SpliceDonor <chr>,
#   UpstreamSpliceAcceptor <chr>, IntronType <chr>, Steepness <dbl>,
#   LowerLimit <dbl>, UpperLimit <dbl>, ED50_Branaplam <dbl>, ED50_C2C5 <dbl>,
#   ED50_Risdiplam <dbl>, spearman.coef.Branaplam <dbl>,
#   spearman.coef.C2C5 <dbl>, spearman.coef.Risdiplam <dbl>,
#   EC.Ratio.Test.Estimate_Branaplam.C2C5 <dbl>,
#   EC.Ratio.Test.Estimate_Branaplam.Risdiplam <dbl>, …
GA.GT.Introns.OfInterest <- 
  GA.GT.Introns %>%
  separate_rows(gene_names, gene_ids, sep=',') %>%
  filter(gene_names %in% c("FOXM1", "LRRK2", "HTT", "STAT1")) %>%
  pull(junc)


GeneExpressionData.tidy %>%
  filter(hgnc_symbol %in% c("FOXM1", "LRRK2", "HTT", "STAT1")) %>%
  ggplot(aes(color=treatment)) +
  geom_line(aes(x=dose.nM, y=log2(CPM))) +
  scale_x_continuous(trans="log1p", limits=c(0, 10000), breaks=c(10000, 3160, 1000, 316, 100, 31.6, 10, 3.16, 1, 0.316, 0)) +
  facet_wrap(~hgnc_symbol, scales = "free_y") +
  theme_bw() +
  theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1, size=3)) +
  labs(title="Dose response effects on gene expression genes of interest")

…oops, with the x-axis in nanomolar units its kind of annoying to interpret. Let’s just plot the x-axis dose rank, instead of nanomolar units, since each of the titration curves cover similar effective dosages.

GeneExpressionData.tidy %>%
  filter(hgnc_symbol %in% c("FOXM1", "LRRK2", "HTT", "STAT1")) %>%
  group_by(treatment) %>%
  mutate(DoseRank = dense_rank(dose.nM)) %>%
  ungroup() %>%
  ggplot(aes(color=treatment)) +
  geom_line(aes(x=DoseRank, y=log2(CPM))) +
  facet_wrap(~hgnc_symbol, scales = "free_y") +
  theme_bw() +
  labs(title="Dose response effects on gene expression genes of interest")

SplicingData.tidy <- read_tsv("../code/DoseResponseData/LCL/TidySplicingDoseData.txt.gz")

SplicingData.tidy %>%
  filter(junc %in% GA.GT.Introns.OfInterest) %>%
  group_by(treatment) %>%
  mutate(DoseRank = dense_rank(dose.nM)) %>%
  ungroup() %>%
  mutate(label = paste(gene_names, junc, sep='\n')) %>%
  ggplot(aes(color=treatment)) +
  geom_line(aes(x=DoseRank, y=PSI)) +
  facet_wrap(~label, scales = "free_y") +
  theme_bw() +
  theme(strip.text.x = element_text(size = 5)) +
  labs(title="All GAGT introns in genes of interest", y="Intronic PSI")

Ok, so I can see the dose response gene expression effects on those genes. And also here are the dose response splicing effects for all the detected GAGT introns (and passed default leafcutter cluster filters) in those genes. The intron in FOXM1 is not in the large table because it didn’t pass my filters for specific splicing effects, and that seems understandable looking at the splicing data. The LRRK2 intron isn’t in the long table because I didn’t even attempt fitting a dose-response curve to the splicing data because I filtered for introns with spearman > 0.9 in at least one treatment. Perhaps that is a bit too strict to capture these introns that only start to show effects at the high doses.


sessionInfo()
R version 4.2.0 (2022-04-22)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)

Matrix products: default
BLAS/LAPACK: /software/openblas-0.3.13-el7-x86_64/lib/libopenblas_haswellp-r0.3.13.so

locale:
 [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C         LC_TIME=C           
 [4] LC_COLLATE=C         LC_MONETARY=C        LC_MESSAGES=C       
 [7] LC_PAPER=C           LC_NAME=C            LC_ADDRESS=C        
[10] LC_TELEPHONE=C       LC_MEASUREMENT=C     LC_IDENTIFICATION=C 

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] forcats_0.5.1   stringr_1.4.0   dplyr_1.0.9     purrr_0.3.4    
[5] readr_2.1.2     tidyr_1.2.0     tibble_3.1.7    ggplot2_3.3.6  
[9] tidyverse_1.3.1

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.8.3     lubridate_1.8.0  assertthat_0.2.1 rprojroot_2.0.3 
 [5] digest_0.6.29    utf8_1.2.2       R6_2.5.1         cellranger_1.1.0
 [9] backports_1.4.1  reprex_2.0.1     evaluate_0.15    highr_0.9       
[13] httr_1.4.3       pillar_1.7.0     rlang_1.0.2      readxl_1.4.0    
[17] rstudioapi_0.13  whisker_0.4      jquerylib_0.1.4  rmarkdown_2.14  
[21] labeling_0.4.2   bit_4.0.4        munsell_0.5.0    broom_0.8.0     
[25] compiler_4.2.0   httpuv_1.6.5     modelr_0.1.8     xfun_0.30       
[29] pkgconfig_2.0.3  htmltools_0.5.2  tidyselect_1.1.2 workflowr_1.7.0 
[33] fansi_1.0.3      crayon_1.5.1     tzdb_0.3.0       dbplyr_2.1.1    
[37] withr_2.5.0      later_1.3.0      grid_4.2.0       jsonlite_1.8.0  
[41] gtable_0.3.0     lifecycle_1.0.1  DBI_1.1.2        git2r_0.30.1    
[45] magrittr_2.0.3   scales_1.2.0     vroom_1.5.7      cli_3.3.0       
[49] stringi_1.7.6    farver_2.1.0     fs_1.5.2         promises_1.2.0.1
[53] xml2_1.3.3       bslib_0.3.1      ellipsis_0.3.2   generics_0.1.2  
[57] vctrs_0.4.1      tools_4.2.0      bit64_4.0.5      glue_1.6.2      
[61] hms_1.1.1        parallel_4.2.0   fastmap_1.1.0    yaml_2.3.5      
[65] colorspace_2.0-3 rvest_1.0.2      knitr_1.39       haven_2.5.0     
[69] sass_0.4.1